Method and Apparatus for Predicting Pulsed Field Ablation Durability

2. The apparatus of claim 1, wherein the example PFA outcomes comprise arterial fibrillation (AFib) recurrence data.

3. The apparatus of claim 1, wherein the example PFA outcomes comprise AFib burden data.

4. The apparatus of claim 1, wherein the example PFA outcomes comprise historical post-PFA procedure ECG data.

5. The apparatus of claim 4, wherein the historical post-PFA procedure ECG data comprises historical post-PFA procedure ECG data captured at least 1 day after, 2 days after, 3 days after, 4 days after, 5 days after, 6 days after, 1 week after, 2 weeks after, 3 weeks after, 4 weeks after, 6 weeks after, 2 months after, 3 months after, 6 months after, 9 months after, 1 year after, 2 years after, or 2 years after a historical PFA procedure.

7. The apparatus of claim 1, wherein the PFA device parameter is of a type selected from a list consisting of voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and pulse delivery phase.

9. The apparatus of claim 1, wherein the training dataset further comprises in-procedure ECG data and in-procedure EGM data.

10. The apparatus of claim 1, wherein the PFA durability machine learning model comprises a multimodal neural network.

12. The method of claim 11, wherein the example PFA outcomes comprise arterial fibrillation (AFib) recurrence data.

13. The method of claim 11, wherein the example PFA outcomes comprise AFib burden data.

14. The method of claim 11, wherein the example PFA outcomes comprise historical post-PFA procedure ECG data.

15. The method of claim 14, wherein the historical post-PFA procedure ECG data comprises historical post-PFA procedure ECG data captured at least 1 day after, 2 days after, 3 days after, 4 days after, 5 days after, 6 days after, 1 week after, 2 weeks after, 3 weeks after, 4 weeks after, 6 weeks after, 2 months after, 3 months after, 6 months after, 9 months after, 1 year after, 2 years after, or 2 years after a historical PFA procedure.

17. The method of claim 11, wherein the PFA device parameter is of a type selected from a list consisting of voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and pulse delivery phase.

19. The method of claim 11, wherein the training dataset further comprises in-procedure ECG data and in-procedure EGM data.

20. The method of claim 11, wherein the PFA durability machine learning model comprises a multimodal neural network.